Reliable sensor data are essential for the effective operation and safety of cyber-physical systems (CPS) in industrial environments. However, sensors frequently experience faults or degradation, leading to compromised system performance. In order to increase the resilience of CPS, this paper proposes a novel approach to creating virtual sensors capable of reconstructing missing or faulty sensor data through gradient-based input reconstruction by leveraging neural networks. Specifically, we employ an LSTM-based autoencoder architecture trained both conventionally and with a masking strategy to handle potential sensor data loss scenarios effectively. Our method involves using automatic differentiation and gradient descent to iteratively optimize missing sensor inputs, guided by the pretrained network. We evaluate this approach comprehensively on both simulated and real-world plant data from cyber-phyiscal process plants, demonstrating robust reconstruction performance across various sensor failure scenarios. Additionally, we explore the efficacy of modular clustering methods versus single comprehensive models, highlighting the advantages and limitations inherent to each approach. Our findings reveal significant potential for improving system resilience and maintaining operational continuity in CPS through advanced virtual sensor implementations.

The rapid evolution and continuously growing complexity of cyber-physical systems (CPS), ranging from Industry 4.0 production plants to ship drivetrains and building monitoring, have led to the abundant generation of heterogeneous, poorly-structured, and not standardized data. This situation is further aggravated by retrofitting legacy systems with new sensors for the purpose of data-driven monitoring. In this paper, we introduce Cyber-Physical System Watch (CPSWatch), a lightweight framework that aims to support the monitoring of CPS including the possibility for diagnosis, encompassing a high-level ontology, two sample datasets of different complexity as well as a use case scenario on how it can be applied. Our proposed ontology provides a unified framework for describing data across different CPS applications and aligns with OPC UA, ensuring its general applicability in modern industrial settings. CPSWatch is evaluated in terms of standard ontology evaluation measures, within the scope of condition monitoring of an automation system in the maritime domain and a benchmark in the field of process engineering.

With the increasing complexity of highly automated cyber-physical systems (CPS), monitoring their behavior has become crucial. Failures in these systems can be costly, halt production, or even pose risks to human safety. Effective diagnosis depends on understanding the system's components, connections, and the influences among them, knowledge typically provided by experts. However, the shift towards self-diagnosing systems necessitates this knowledge be machine-readable and interpretable. This paper introduces a novel methodology that utilizes an ontology to encode knowledge about cyber-physical systems and systematically generate propositional logical expressions. These expressions can then be evaluated using state-of-the-art diagnostic algorithms to identify failure causes. Our methodology was validated using an established AI benchmark for diagnostics. We constructed an ontology description for the underlying cyber-physical system, deduced influences of system sensors from data, and successfully diagnosed induced failures, demonstrating the efficacy and applicability of our approach.